The overall objective of this proposal is to use a comprehensive set of experimental and computational modeling techniques to improve our understanding of normal and anesthetic-modified GABA(A)R function. GABA(A)Rs are responsible for the majority of fast inhibitory neural transmission in the central nervous system and are the target for a variety of general anesthetics. Despite the importance of GABA(A)Rs in both normal and clinical-altered neural function, no consensus computational model exists for either the primary or the anesthetic-modified receptor kinetics. Computational models that describe GABA(A)Rs are invaluable in predicting the time course of synaptic events, the structural transitions of the receptor and in understanding how these properties are altered by the binding of molecules, such as anesthetics. To derive kinetic models of receptor function, local versus global optimization methods will be compared for their ability to estimate a kinetic model from macroscopic and single-channel currents of known, simulated models. The global optimization methods are expected to estimate a kinetic model with greater accuracy and precision than the widely-used local methods. The most efficient optimization method will then be used to estimate kinetic models for the normal and anesthetic-modified GABA(A)R from experimental macroscopic current responses to a variety of stimulus protocols. More complex pulse protocols are expected to unmask kinetic features of the GABA(A)R that are not visible with traditional step protocols. GABA(A)R models will then be refined by fitting to experimental single-channel activity. It is expected that a discrete, overlapping model space will exist, in which both macroscopic and single-channel activity are predicted and in which one comprehensive kinetic model of GABA(A)R function can be derived. At the conclusion of these aims, significant improvements will have been made to the kinetic models and, therefore, to our understanding of both normal and anesthetic-modified GABA(A)R function. These models will lead to predictions of how anesthetics alter the kinetics and structural transitions of GABA(A)R and can be incorporated into cellular models to further our understanding of how anesthetics alter neural activity. Thus, the results of this proposal may ultimately suggest better methods to control the state of anesthesia .